{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T03:46:14Z","timestamp":1768967174031,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Finance Science and Technology Project of Hainan Province","award":["ZDYF2018231"],"award-info":[{"award-number":["ZDYF2018231"]}]},{"name":"the Major Projects of High Resolution Earth Observation Systems of National Science and Technology","award":["05-Y30B01-9001-19\/20-1"],"award-info":[{"award-number":["05-Y30B01-9001-19\/20-1"]}]},{"name":"Sichuan Province Science and Technology Program","award":["2018JZ0054"],"award-info":[{"award-number":["2018JZ0054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the \u2018\u2018Multi-resolution Segmentation\u2019\u2019 algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.<\/jats:p>","DOI":"10.3390\/rs12183005","type":"journal-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T10:24:09Z","timestamp":1600165449000},"page":"3005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Maofan","family":"Zhao","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101400, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyan","family":"Meng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5073-1694","authenticated-orcid":false,"given":"Linlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Die","family":"Hu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101400, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101400, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5835-1829","authenticated-orcid":false,"given":"Mona","family":"Allam","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"},{"name":"Environment &amp; Climate Changes Research Institute, National Water Research Center, El Qanater El khairiya 13621\/5, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111706","DOI":"10.1016\/j.rse.2020.111706","article-title":"Open water detection in urban environments using high spatial resolution remote sensing imagery","volume":"242","author":"Chen","year":"2020","journal-title":"Remote Sens. 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